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Date:         Thu, 1 Oct 2009 12:03:23 -0700
Reply-To:     Paige Miller <paige.miller@KODAK.COM>
Sender:       "SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>
From:         Paige Miller <paige.miller@KODAK.COM>
Organization: http://groups.google.com
Subject:      Re: OLS estimates on clusterred data
Comments: To: sas-l@uga.edu
Content-Type: text/plain; charset=ISO-8859-1

On Oct 1, 2:40 pm, Tony <tony.cross...@gmail.com> wrote: > Thanks! > > Can i just do the following? > > PROC MIXED DATA=imputdata NOitprint; > CLASS year state; > MODEL Y= X; > RUN;

Unless you put Year and State in the model statement, you have a simple linear regression of Y versus X.

Even if you put Year and State in the model, this doesn't account for the claimed serial correlation over the years, nor does it account for any clustering, which I assume is different than the serial correlation you are referring to, but which you don't explain further.

There are many different types of covariance structures possible in PROC MIXED, including some that deal with autocorrelation. Check the docs to see if one of them meets your needs.

-- Paige Miller paige\dot\miller \at\ kodak\dot\com


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